A cognitive load theoretic framework for optimal decision-support to visualize data in managerial accounting

The need to deal with an increasing amount of data and different data sources changes the way human beings’ process information. In this context, the visual transformation of data has tied up a considerable amount of resources in academia and practice. New ways of visualizing high amounts of data have been proposed in recent years by visualization researchers; however, their use is still scare and their usability is rarely tested, particularly in managerial accounting. Thus, the aim of this cumulative dissertation consisting of eight published papers is twofold: First, the current and future visualization use needs to be defined for managerial accounting, and second, a theoretical model or framework to test the corresponding usability needs to be identified and applied. To this end, a comprehensive literature review, a document analysis, a survey, three laboratory experiments, and a quasi-experiment were conducted. The results indicated that visualization use is in a state of upheaval, particularly in internal communication (management reporting), from static and traditional visualization use towards the integration of interaction as well as newer forms of visualization to deal with big data. Thus, the current theoretical framework needs to be adapted to these new requirements for a better empirical fit, leading me towards a new research model based on information processing and cognitive load theory. The model integrates the important fit between the task and the visualization by relying on an interval-scaled variable, namely objective task complexity, and it accounts for individual differences on the basis of the user by introducing an innovative method for testing the situational cognitive load by relying on eye-tracking data.